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    "# 多层感知机\n",
    "本篇开始介绍多层感知机，首先回忆一下之前介绍的线性回归和softmax回归模型（逻辑回归只是特殊的softmax回归）：\n",
    "+ 线性回归：输入经过一个线性变换直接得到输出\n",
    "+ softmax回归：输入经过一个线性变换，再经过一个sigmod函数得到输出\n",
    "\n",
    "我们进一步思考，假设输入到输出之间不止有一次线性变换会怎么样？我们以逻辑回归为例：\n",
    "+ softmax回归：输入经过一个线性变换得到一个向量，再经过一个线性变换得到一个向量，......，再经过一个sigmod函数得到输出\n",
    "\n",
    "其实如果只是这样简单的增加多层线性变换，通过数学推导可以得知相当于一层线性变换，只是对应的参数变多而已。所以我们的关键是在与线性变换之后需要增加一点非线性变换，那么就需要一种变换将输出变一下，这种变换就是激活函数，这样就变成了：    \n",
    "+ softmax回归：输入经过一个线性变换+激活函数得到一个向量，再经过一个线性变换+激活函数得到一个向量，......，再经过一个sigmod函数得到输出\n",
    "\n",
    "总结来说，前面介绍的线性回归和softmax回归模型都是经典的机器学习模型，它们从输入到输出中间只有一层变换。从本篇开始正式进入深度学习的领域，开始介绍多层感知机，深度体现在输入到输出直接会有多层变换。多层感知机特殊之处在于中间的变换都是全连接变换。\n",
    "经过上面分析，下面给出多层感知机的模型构成：\n",
    "\n",
    "## 1. 模型构成\n",
    "+ 数据：和线性回归、softmax回归模型一样\n",
    "+ 模型：最后一层和线性回归、softmax回归模型是一样的，只不过首层会经过多次全连接层+激活函数得到中间层，一直到倒数第一层\n",
    "+ 参数：和线性回归、softmax回归模型一样\n",
    "+ 损失函数：和线性回归、softmax回归模型一样\n",
    "+ 优化方法：梯度下降等，得益于 PyTorch 自动求梯度，我们不再需要显示求出损失函数的导数\n",
    "\n",
    "下面重点认识一下激活函数。\n",
    "\n",
    "## 2. 激活函数\n",
    "常用的激活函数主要有：sigmoid、relu、tanh，下面我们用代码分别绘制它们的曲线和函数导数曲线。"
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S9KrOlI7vBvanMIx575TS2JTSCOB44CFgRkRc3IV91gNTgO+klI4ANgCfb98opTQzpTQ1pTR1r7326sLLS5Kq0ar1m/nP3z3OtOvu5Y9PreKjpxzAvZ89mQ8dP8FikapCW7EoIg5u/1xEnFTaZifUAxOBk4ALgO8V15TsKA5zMKlGbVv02hFGkujclLTTUkot7TemlF4Cfg38OiIaurDPZqA5pfSX4uNf0UHBSJIkgJc3tzDz3sV8//4ltOTyXHD0OD566gGMGNQ369Ck7vLLiPgJcBWFKWlXAVOBN++g/QpgbMnjMcVtpZqBvxRzuiURsZBCAWne7gxcUmVrKU5Jq3OEkSQ6McKorVgUEae1fy4i3l/apjNSSs8ByyNicnHTqWw/x16SJDa35LjpvsWceNXdfOvuJk47eCR3fupEvvLOQywWqdodQ6EA9GcKBZ1ngeNeo/08YGJETCheTOR8oP06R7+jMLqI4rS2ScDi3Rq1pIrXNsKowRFGkujaotdXRMQ5wGeAgcBNFNYz+tFO7PejwM+KSc1i4IM78RqSpCqUyyd++7cVfG3uQlas3cQJE4fz2TMO5NAxg7MOTSqXFgpXR+tHYYTRkpRSfkeNU0qtEXEZcAeF9YluTin9IyK+DMwvLpJ9B3B6RDwJ5IDLU0ovdndHJFWW1rZFrx1hJImuFYxOBD4N/L34+IqU0s93Zqcppb9TGFotSRIAKSXuemoVV9+xgAXPr+ewMYO56t2HcdwBHa3xK1W1eRSulHYUMBz4bkSck1I6d0d/kFK6Dbit3bYrSu4n4FPFmyR1qNVFryWV6ErBaCiFy7Y+TWFu/L4REcUERJKknTZ/6UtceXsj85etYcLwAdxw4RTOPHRvIkxYVZM+lFKaX7y/EjgrIt6bZUCSakPbCKM6p6RJomsFo4eAK1NKN0dEP2AG8ABwbLdEJkmqegueW8/VdzRy51OrGDGoD189+xDOmzqWhjoTVdWeiBiYUnqlpFi0TUrpJ6Vtyh+dpFrQsu0qaZ6wkdS1gtFpKaVnAFJKm4CPRcRbuicsSVI1W7F2E1+bu5BfP9LMwN71XH7GZD543Hj69+7Kz5JUdX4fEX+nMB3t4ZTSBoCI2I/CgtXvAb5H4QqzkrTb5fJtU9I8cSOpEwWjiBifUlraViwqlVL6UxTmC4xOKTV3S4SSpKqxZsNWbri7iR8/tAyAfzl+Av9+0gEMHdA748ik7KWUTo2IM4F/BY6LiKFAK7AAuBV4f/Fqs5LULVpyLnot6VWdOZV7dUT0oni2C3iBwhU7DgBOBk4FvghYMJIkdWjj1lZuvn8JN967mA1bWzlnyhg+MW0So4f0yzo0qUfpaPFqSSqXbYteu4aRJDpRMEopnRsRBwMXAZcAoyhc6vUpCme7vppS2tytUUqSKlJLLs8v5i3n+rsW8cL6LZx20Eg+O30yk0YOyjo0qceJiHHFu7mU0opMg5FUk7Yteu0II0l0cg2jlNKTwBe6ORZJUpXI5xO3PbGSa+csZMnqDRw1fijfuWgKU8cPyzo0qSf7UfHfF4F3ZxmIpNrUsm2EkQUjSV1Y9Doi3tfR9pTSj3dfOJKkSvdA02quvL2Rx1esY9LIgdz0vqmcetAICkveSdqRlNLJWccgqba1LXpd76LXkujaVdKOKrnfl8LaRY8AFowkSTyxYh0zZjdy36LVjB7Sj2vOPZyzjxhNnWcppS7xJJ2krLjotaRSnS4YpZQ+Wvo4IoYAt+zugCRJlWXp6g1cM2cBf3hsJUP7N/CfbzuIi9+0L30b6rIOTapUnqSTlInWthFGnuyRRNdGGLW3AZiwuwKRJFWWVes38427FnHLX5fTUNeLj55yAB9+y37s0bch69CkiuZJOklZaW0bYeRV0iTRtTWM/htIxYe9gIOBX3ZHUJKknuvlzS3MvHcx379/CS25POcfPZaPnTqREYP6Zh2aVK08SSepLNpGGDU4JU0SXRthdE3J/VZgWUqpeTfHI0nqoba05vjJg8u44e4m1mxs4e2HjeIzp09m/PABWYcmVRVP0knKSmsuUdcrvFCFJKBraxjd252BSJJ6plw+8du/reBrcxeyYu0mTpg4nM+ecSCHjhmcdWhStfIknaRMtOTzrl8kaZvXLRhFxHpePcu13VNASintsdujkiRlLqXEHxtXcdXsBSx4fj2Hjh7MjHMO4/iJw7MOTapqnqSTlJXWXLJgJGmb1y0YpZQGlSMQSVLP8fCyl7jy9kbmLV3D+D37860Lj+DMQ0bRyyRS6jaepJOUtdZcnvo6F7yWVNClq6RFxPHAxJTSDyJiODAopbRkZ3YcEXXAfGBFSuntO/MakqTda+Hz67lq9gLufOp59hrUh//9zkN4z1FjaTB5lLrdrpyki4jpwPVAHXBTSunKHbQ7B/gVcFRKaf7O7k9SdWrNJxe8lrRNV66S9kVgKjAZ+AHQG/gpcNxO7vvjwFOAZ8skKWMr1m7ia3MX8ptHmhnQu57PnD6JS46fQP/eXTqvIGk36cpJuuJJuBuAaUAzMC8iZqWUnmzXbhCF/Osv3Ru9pEpVmJLmSSJJBV35n8DZwBHAIwAppWeLiUeXRcQY4G3AV4FP7cxrSJJ23ZoNW/n2PU386MFlkOCS4ybwkZMPYOiA3lmHJtWsnThJdzTQlFJaXPz7W4CzgCfbtfsKMAO4vBvCllQFWvJ56px+LqmoKwWjrSmlFBEJICJ25TrKXwc+C+yw4BQRlwKXAowbN24XdiVJam/j1lZ+8MBSvnvP07yytZVzpozhk9MmMXpIv6xDk9T1k3SjgeUlj5uBY0obRMQUYGxK6daIeM2CkTmYVLtac05Jk/SqThWMIiKAP0TEjcCQiPgwcAnwva7uMCLeDqxKKT0cESftqF1KaSYwE2Dq1KkdLQApSeqillyeX8xbzvV3LeKF9Vs47aARXH7GgUze2+sbSD3I7jxJR0T0Aq4DPtCZ9uZgUu3K5ZOLXkvaplMFo2LSci6F6WMvUxgifUVKae5O7PM44B0RcSbQF9gjIn6aUrp4J15LktQJKSVufXwl185ZyJLVG5i671C+fdEUjho/LOvQJJXYyZN0K4CxJY/HFLe1GQQcAtxTeHn2BmZFxDtc+FpSqZZcnnqnpEkq6sqUtEeAtSmlXZr3nlL6D+A/AIojjD5jsUiSus8DTauZMbuRx5rXMWnkQG5631ROPWgExf84SupBdvIk3TxgYkRMoFAoOh+4sOQ11wHD2x5HxD0U8i+LRZK2U7hKmiOMJBV0pWB0DHBRRCwDNrRtTCkdttujkiTtsidWrGPG7EbuW7Sa0UP6cc25h3P2EaNdzFLq+bp0ki6l1BoRlwF3AHXAzSmlf0TEl4H5KaVZ3RirpCrSknPRa0mv6krB6IzdvfOU0j3APbv7dSWpli1dvYFr5izgD4+tZEj/Bv7zbQdx8Zv2pW9DXdahSeqcLp+kSyndBtzWbtsVO2h70u4JU1K1cdFrSaU6XTBKKS3rzkAkSbtm1frNfPOuJn7+12doqOvFZScfwKUn7scefRuyDk1S1+z2k3SS1Bm5fKK+l1PSJBV0ZYSRJKkHWr+5hZl/WsxN9y1hay7PBUeP5WOnTGTEHn2zDk3STvAknaSstOTz9Gnwv4iSCvw2kKQKtaU1x08feoZv/XERaza28LbDRvGZ0yczYfguXYFbkiTVqNZc8ippkraxYCRJFSaXT/zubyu4bu5CVqzdxPEHDOdz0w/k0DGDsw5NkiRVsJZcnnqvkiapyIKRJFWIlBJ/bFzFVbMXsOD59Rwyeg+uPOdQTpi4V9ahSZKkKpDLu+i1pFdZMJKkCvDwspe48vZG5i1dw/g9+/PNC47gbYeOopfDxiVJ0m7S6qLXkkpYMJKkHmzh8+u5avYC7nzqeYYP7MNX3nkI5x81lgaHi0uSpN2sMCXNk1GSCiwYSVIP9OzaTXxt7kJ+/Ugz/XvX85nTJ3HJ8RPo39uvbUmS1D1c9FpSKf/nIUk9yJoNW/n2PU386MFlkOCDx03gIycfwLABvbMOTZIkVbnWvIteS3qVBSNJ6gE2bc1x8wNL+O69T/PKllbedcQYPjltImOG9s86NEmSVCNa84kGRxhJKrJgJEkZasnl+eX85Vx/5yJWrd/CaQeN4PIzDmTy3oOyDk2SJNWY1lxyhJGkbSwYSVIGUkrc9vhzXDNnAUtWb+DIfYdyw0VTOGr8sKxDkyRJNcpFryWVsmAkSWX256bVzJjdyKPN65g4YiDfe99UTjtoBBEmaJIkKTuteRe9lvQqC0aSVCZPrFjHjNmN3LdoNfsM7svV7z6Md00ZQ52JmSRJylhKiVw+Ud/LKWmSCiwYSVI3W/biBq6Zs5D/fvRZhvRv4AtnHsR737wvfRvqsg5NkiQJKIwuAmhwSpqkorIXjCJiLPBjYCSQgJkppevLHYckdbdV6zfzzbua+Plfn6G+LvjIyfvzryfuzx59G7IOTVKViYjpwPVAHXBTSunKds9/CvgXoBV4AbgkpbSs7IFK6rFac4WCkYteS2qTxQijVuDTKaVHImIQ8HBEzE0pPZlBLJK0263f3ML3/rSYm+5fwpbWPOcfNZaPnzqREXv0zTo0SVUoIuqAG4BpQDMwLyJmtcut/gZMTSltjIh/A64C3lP+aCX1VC35PIBrGEnapuwFo5TSSmBl8f76iHgKGA1YMJJU0ba05vjpQ89ww91NvLRhK287dBSfPn0S++01MOvQJFW3o4GmlNJigIi4BTiLktwqpXR3SfuHgIvLGqGkHm/bCCMLRpKKMl3DKCLGA0cAf8kyDknaFbl84nd/W8F1cxeyYu0mjjtgTz43/UAOGzMk69Ak1YbRwPKSx83AMa/R/kPA7d0akaSK09o2wsgpaZKKMisYRcRA4NfAJ1JKL3fw/KXApQDjxo0rc3SS9PpSSty9YBVXzV5A43PrOWT0Hlx5zqGcMHGvrEOTpA5FxMXAVODE12hjDibVoLYRRi56LalNJgWjiGigUCz6WUrpNx21SSnNBGYCTJ06NZUxPEl6XQ8vW8OM2xv569KX2HfP/nzzgiN426Gj6OUwbknltwIYW/J4THHbdiLiNOALwIkppS07ejFzMKk2vTolzRFGkgqyuEpaAN8HnkopXVfu/UvSrlj0/HquumMBc598nuED+/CVs97A+UePo8Hh25KyMw+YGBETKBSKzgcuLG0QEUcANwLTU0qryh+ipJ5u26LXjjCSVJTFCKPjgPcCj0fE34vb/ldK6bYMYpGkTnl27Sa+fudCfvVwM/171/PpaZO45PgJDOiT6VJwkkRKqTUiLgPuAOqAm1NK/4iILwPzU0qzgKuBgcB/Fc7d8UxK6R2ZBS2px3GEkaT2srhK2v2AZWtJFWHtxq18+56n+eGfl0KCDx43gY+cfADDBvTOOjRJ2qZ44u22dtuuKLl/WtmDklRRWh1hJKkdT41LUgc2bc1x8wNL+O69T/PKllbOPmI0n5o2iTFD+2cdmiRJ0m7noteS2rNgJEklWnJ5fjl/OdffuYhV67dw6oEjuHz6ZA7ce4+sQ5MkSeo2bSOM6pySJqnIgpEkASklbn/iOa65YwGLV29gyrghfOvCKRw9YVjWoUmSJHW7lrYRRl7xVVKRBSNJNe/PTauZMbuRR5vXMXHEQGa+90imHTyS4sKwkiRJVS+XLy567ZVfJRVZMJJUs55YsY4Zsxu5b9Fq9hncl6vefRjnTBlDnWfWJElSjWnJuei1pO1ZMJJUc5a9uIFr5yxk1qPPMrhfA1848yDe++Z96dtQl3VokiRJmdi26LVrGEkqsmAkqWa8sH4L3/zjIv7fX56hvi7495P2519P3J/B/RqyDk2SJClTry567QgjSQUWjCRVvfWbW/jenxZz0/1L2NKa5z1HjeXjp05k5B59sw5NkiSpR9i26LVT0iQVWTCSVLW2tOb42UPP8K27m3hpw1bedugoPn36JPbba2DWoUmSJPUoLnotqT0LRpKqTi6f+P3fV3Dd3IU0r9nEsfvvyeemH8jhY4dkHZokSVKPtG3Ra6ekSSqyYCSpaqSUuHvBKq6avYDG59bzhn324P+cfSgnTBxOhMmPJEnSjrRuG2FkziSpwIKRpKrw8LI1zLi9kb8ufYl99+zPNy44grcfOopeniWTJEl6Xa3bRhg5JU1SgQUjSRWtadV6rpq9gDlPPs/wgb358llv4PyjxtG73mRHkiSps9pGGLnotaQ2FowkVaRn127i63cu5FcPN9O/dz2fmjaJDx0/gQF9/FqTJEnqqtaci15L2p7/s5JUUdZu3Mq373maH/55KST4wLET+MjJ+7PnwD5ZhyZJklSxWvIuei1pexaMJFWETVtz/ODPS/jOPU/zypZWzn7jaD45bRJjh/XPOjRJkqSKt22EkQUjSUWZFIwiYjpwPVAH3JRSujKLOCT1fK25PL+c38z1dy3k+Ze3cMqBI7j8jMkcNGqPrEOTpB7j9XKriOgD/Bg4EngReE9KaWm545TUc7Utel1nwUhSUdkLRhFRB9wATAOagXkRMSul9GS5Y5HUc6WUmP3Ec1x9xwIWr97AlHFD+OYFUzh6wrCsQ5OkHqWTudWHgDUppQMi4nxgBvCe8kcrqadqzSca6oIIC0aSCrIYYXQ00JRSWgwQEbcAZwFlLxg9u3YTueLVACT1HEtWb+DauQt5dPlaDhgxkJnvPZJpB480gZGkjnUmtzoL+FLx/q+Ab0VEpJTKngi15PI8t25zuXcr6XWs2biV+l4ueC3pVVkUjEYDy0seNwPHZBAH75n5IMtf2pTFriW9jlGD+3LVOYfxrimjvVqHJL22zuRW29qklFojYh2wJ7C6LBGWWLFmEyddc0+5dyupE/Yc0DvrECT1ID120euIuBS4FGDcuHHdso//9daD2LA11y2vLWnn9Wuo49SDRtC3oS7rUCSp5nR3DrbnwN5cc+7hu/11Je26/fcakHUIknqQLApGK4CxJY/HFLdtJ6U0E5gJMHXq1G4ZLv3WQ0d1x8tKkiSVU2dyq7Y2zRFRDwymsPj1P+nuHGxQ3wbefeSY3f2ykiRpN8tinsc8YGJETIiI3sD5wKwM4pAkSaoGncmtZgHvL95/N/DHLNYvkiRJlaPsI4yK8+YvA+6gcOnXm1NK/yh3HJIkSdVgR7lVRHwZmJ9SmgV8H/hJRDQBL1EoKkmSJO1QJmsYpZRuA27LYt+SJEnVpqPcKqV0Rcn9zcC55Y5LkiRVLi89JEmSJEmSpO1EJUxfj4gXgGXd9PLDyeCSshmohX7WQh+hNvpZC30E+1lNaqGP0L393DeltFc3vbZ2kjnYLquFPoL9rCa10EeojX7WQh/Bfu4OO8zBKqJg1J0iYn5KaWrWcXS3WuhnLfQRaqOftdBHsJ/VpBb6CLXTT5VHLbyfaqGPYD+rSS30EWqjn7XQR7Cf3c0paZIkSZIkSdqOBSNJkiRJkiRtx4IRzMw6gDKphX7WQh+hNvpZC30E+1lNaqGPUDv9VHnUwvupFvoI9rOa1EIfoTb6WQt9BPvZrWp+DSNJkiRJkiRtzxFGkiRJkiRJ2k5NFIwi4tyI+EdE5CNiarvn/iMimiJiQUScsYO/nxARfym2+0VE9C5P5DunGOPfi7elEfH3HbRbGhGPF9vNL3OYuywivhQRK0r6euYO2k0vHt+miPh8uePcVRFxdUQ0RsRjEfHbiBiyg3YVdzxf79hERJ/i+7mp+Bkcn0GYuyQixkbE3RHxZPF76OMdtDkpItaVvJevyCLWXfV678Eo+EbxeD4WEVOyiHNnRcTkkmP094h4OSI+0a5NRR7LiLg5IlZFxBMl24ZFxNyIWFT8d+gO/vb9xTaLIuL95YtaPV2t5V9gDtZBu4rNwao5/wJzsJI2Ffm7Xara8y8wB8s0B0spVf0NOAiYDNwDTC3ZfjDwKNAHmAA8DdR18Pe/BM4v3v8u8G9Z96kLfb8WuGIHzy0Fhmcd4y707UvAZ16nTV3xuO4H9C4e74Ozjr2L/TwdqC/enwHMqIbj2ZljA/w78N3i/fOBX2Qd9070cxQwpXh/ELCwg36eBPwh61h3Q19f8z0InAncDgTwJuAvWce8C32tA54D9q2GYwm8BZgCPFGy7Srg88X7n+/ouwcYBiwu/ju0eH9o1v3x1jNutZx/FWM2B6vgHKxa86/OHhtzsMq51VL+VeyPOVgqXw5WEyOMUkpPpZQWdPDUWcAtKaUtKaUlQBNwdGmDiAjgFOBXxU0/At7ZjeHuNsXYzwN+nnUsGToaaEopLU4pbQVuoXDcK0ZKaU5KqbX48CFgTJbx7EadOTZnUfjMQeEzeGrxfV0xUkorU0qPFO+vB54CRmcbVWbOAn6cCh4ChkTEqKyD2kmnAk+nlJZlHcjukFL6E/BSu82ln78d/fadAcxNKb2UUloDzAWmd1ecqiy1mn+BOVhRRedgVZx/gTlYramm/AvMwdqUJQeriYLRaxgNLC953Mw/f4nsCawt+cHoqE1PdQLwfEpp0Q6eT8CciHg4Ii4tY1y702XFoZU372CoXmeOcSW5hMIZgo5U2vHszLHZ1qb4GVxH4TNZkYrDuY8A/tLB02+OiEcj4vaIeEN5I9ttXu89WE2fx/PZ8X8Eq+FYAoxMKa0s3n8OGNlBm2o6piqfas+/wBwMquv7oZryLzAHa6/Sf7drKf8Cc7A2ZTmu9bv7BbMSEXcCe3fw1BdSSr8vdzzdrZP9vYDXPrN1fEppRUSMAOZGRGOxwtljvFY/ge8AX6HwJfkVCkO/LylfdLtPZ45nRHwBaAV+toOX6fHHs5ZFxEDg18AnUkovt3v6EQrDal+JwjoQvwMmljnE3aEm3oNRWEflHcB/dPB0tRzL7aSUUkR4WVX9k1rLv8AcjCrKwcy/akMN5GA18x40Byu/qikYpZRO24k/WwGMLXk8prit1IsUhu3VF6vrHbUpu9frb0TUA+8CjnyN11hR/HdVRPyWwvDUHvXl0tnjGhHfA/7QwVOdOcaZ68Tx/ADwduDUVJy02sFr9Pjj2U5njk1bm+bie3owhc9kRYmIBgqJys9SSr9p/3xp8pJSui0ivh0Rw1NKq8sZ567qxHuwIj6PnfBW4JGU0vPtn6iWY1n0fESMSimtLA5dX9VBmxUU1gxoM4bCejWqEbWWf4E5WHuVnIPVaP4F5mDbVMPvdg3lX2AOVqosOVitT0mbBZwfhasATKBQgfxraYPij8PdwLuLm94PVMIZs9OAxpRSc0dPRsSAiBjUdp/Cwn5PdNS2p2o39/ZsOo5/HjAxClda6U1hCOOscsS3u0TEdOCzwDtSSht30KYSj2dnjs0sCp85KHwG/7ijhK2nKs73/z7wVErpuh202bttXYCIOJrCd3NFJWWdfA/OAt4XBW8C1pUMt60kOxw5UA3HskTp529Hv313AKdHxNDilJTTi9uk11LN+ReYg7Wp6BysivMvMAcrbVPRv9s1ln+BOVip8uRgqQesDN7dNwo/ZM3AFuB54I6S575A4SoBC4C3lmy/DdineH8/ColME/BfQJ+s+9SJPv8Q+J/ttu0D3FbSp0eLt39QGHqbedxd7ONPgMeBxyh8qEa172fx8ZkUrorwdIX2s4nC/NS/F29tV6yo+OPZ0bEBvkwhOQPoW/zMNRU/g/tlHfNO9PF4CkP2Hys5hmcC/7PtMwpcVjxuj1JYWPPYrOPeiX52+B5s188Abige78cpuWpSpdyAARSSj8El2yr+WFJIvlYCLcXfyw9RWKviLmARcCcwrNh2KnBTyd9eUvyMNgEfzLov3nrOjRrMv4px/xBzsLZ2FZuDUcX5146ODeZgFfO7XdLHmsi/iv0wB8sgB4vijiRJkiRJkiTAKWmSJEmSJElqx4KRJEmSJEmStmPBSJIkSZIkSduxYCRJkiRJkqTtWDCSJEmSJEnSdiwYSZIkSZIkaTsWjCRJkiRJkrQdC0aSeoyIOCoiHouIvhExICL+ERGHZB2XJElSNTMHk9SRSCllHYMkbRMR/xvoC/QDmlNK/zfjkCRJkqqeOZik9iwYSepRIqI3MA/YDBybUsplHJIkSVLVMweT1J5T0iT1NHsCA4FBFM5ySZIkqfuZg0najiOMJPUoETELuAWYAIxKKV2WcUiSJElVzxxMUnv1WQcgSW0i4n1AS0rp/0VEHfDniDglpfTHrGOTJEmqVuZgkjriCCNJkiRJkiRtxzWMJEmSJEmStB0LRpIkSZIkSdqOBSNJkiRJkiRtx4KRJEmSJEmStmPBSJIkSZIkSduxYCRJkiRJkqTtWDCSJEmSJEnSdiwYSZIkSZIkaTv/Px/NexioWFeTAAAAAElFTkSuQmCC\n"
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     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "def plot_x_y(x, y, name):\n",
    "    _, figs = plt.subplots(1, 2, figsize=(20, 2.5))\n",
    "    figs[0].plot(x.detach().numpy(), y.detach().numpy())\n",
    "    figs[0].set_xlabel('x')\n",
    "    figs[0].set_ylabel(name + '(x)')\n",
    "    y.sum().backward()\n",
    "    figs[1].plot(x.detach().numpy(), x.grad.detach().numpy())\n",
    "    figs[1].set_xlabel('x')\n",
    "    figs[1].set_ylabel(name + '\\'(x)')\n",
    "    plt.show()\n",
    "\n",
    "x = torch.range(-10, 10, 0.1, requires_grad=True)\n",
    "y = torch.sigmoid(x)\n",
    "plot_x_y(x, y, 'sigmoid')\n",
    "x.grad.zero_()\n",
    "y = torch.relu(x)\n",
    "plot_x_y(x, y, 'relu')\n",
    "x.grad.zero_()\n",
    "y = torch.tanh(x)\n",
    "plot_x_y(x, y, 'tanh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 多层感知机实现\n",
    "设置隐含层的数量为 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import TensorDataset\n",
    "import pytorch_lightning as pl\n",
    "import numpy as np\n",
    "\n",
    "class MultiLayerModel(pl.LightningModule):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(MultiLayerModel, self).__init__()\n",
    "        # 定义模型结构\n",
    "        self.l1 = torch.nn.Linear(28*28, 256)\n",
    "        self.l2 = torch.nn.Linear(256, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 必须：定义模型\n",
    "        x = self.l1(x.view(x.size(0), -1))\n",
    "        x = torch.relu(x)\n",
    "        return torch.log_softmax(self.l2(x), dim=1)\n",
    "\n",
    "    def training_step(self, batch, batch_nb):\n",
    "        # 必须提供：定于训练过程\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = F.nll_loss(y_hat, y)\n",
    "        tensorboard_logs = {'train_loss': loss}\n",
    "        return {'loss': loss, 'log': tensorboard_logs}\n",
    "\n",
    "    def test_step(self, batch, batch_nb):\n",
    "        # 可选提供：定义测试过程\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        return {'test_loss': F.nll_loss(y_hat, y)}\n",
    "\n",
    "    def test_epoch_end(self, outputs):\n",
    "        # 可选提供：定义测试过程\n",
    "        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()\n",
    "        logs = {'test_loss': avg_loss}\n",
    "        return {'test_loss': avg_loss, 'log': logs, 'progress_bar': logs}\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        # 必须提供：定义优化器\n",
    "        # can return multiple optimizers and learning_rate schedulers\n",
    "        # (LBFGS it is automatically supported, no need for closure function)\n",
    "        return torch.optim.SGD(self.parameters(), lr=0.01)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        # 必须提供：提供训练数据集\n",
    "        mnist_train = torchvision.datasets.FashionMNIST('../../datas', train=True, download=True,\n",
    "            transform=transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize((0.1307,), (0.3081,))\n",
    "            ]))\n",
    "        return DataLoader(mnist_train, batch_size=128, shuffle=True, num_workers=4)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        # 可选提供：提供测试数据集\n",
    "        mnist_test = torchvision.datasets.FashionMNIST('../../datas', train=False, download=True,\n",
    "            transform=transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize((0.1307,), (0.3081,))\n",
    "            ]))\n",
    "        return DataLoader(mnist_test, batch_size=128, shuffle=False, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "GPU available: False, used: False\nTPU available: False, using: 0 TPU cores\n\n  | Name | Type   | Params\n--------------------------------\n0 | l1   | Linear | 200 K \n1 | l2   | Linear | 2 K   \nEpoch 20: 100%|██████████| 469/469 [00:04<00:00, 113.60it/s, loss=0.352, v_num=42]\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "ml_model = MultiLayerModel()\n",
    "\n",
    "# most basic trainer, uses good defaults (1 gpu)\n",
    "trainer = pl.Trainer(max_epochs=20)\n",
    "trainer.fit(ml_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Testing:  96%|█████████▌| 76/79 [00:01<00:00, 53.53it/s]--------------------------------------------------------------------------------\nTEST RESULTS\n{'test_loss': tensor(0.3744)}\n--------------------------------------------------------------------------------\nTesting: 100%|██████████| 79/79 [00:01<00:00, 53.06it/s]\n"
    }
   ],
   "source": [
    "res = trainer.test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 查看预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x864 with 10 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 获取每一个图片对应类别的英文解释\n",
    "mnist_test = torchvision.datasets.FashionMNIST('../../datas', train=False, download=True,\n",
    "    transform=transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ]))\n",
    "def get_fashion_mnist_label_name(label_id):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return text_labels[label_id]\n",
    "\n",
    "def show_fasion_mnist_head(head_num, mnist):\n",
    "    _, figs = plt.subplots(1, head_num, figsize=(12, 12))\n",
    "    for i in range(0, head_num):\n",
    "        imgs, t_label_id = mnist[i]\n",
    "        p_label_id = torch.argmax(ml_model(imgs))\n",
    "        f = figs[i]\n",
    "        f.imshow(imgs.view(28, 28))\n",
    "        f.set_title(get_fashion_mnist_label_name(t_label_id) + '\\n' + get_fashion_mnist_label_name(p_label_id))\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "\n",
    "show_fasion_mnist_head(10, mnist_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 查看准确率和混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pytorch_lightning.metrics.functional import confusion_matrix\n",
    "from pytorch_lightning.metrics.functional import accuracy\n",
    "imgs, target = iter(ml_model.test_dataloader()).next()\n",
    "p_labels_logic = ml_model(imgs)\n",
    "p_labels = torch.argmax(p_labels_logic, dim=1)\n",
    "accuracy = accuracy(p_labels, target)\n",
    "cm = confusion_matrix(p_labels, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "accuracy= tensor(0.8750)\nconfusion_matrix=\n tensor([[12.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n        [ 0., 13.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n        [ 1.,  0., 15.,  0.,  0.,  0.,  1.,  0.,  0.,  0.],\n        [ 0.,  0.,  0.,  9.,  1.,  0.,  1.,  0.,  0.,  0.],\n        [ 0.,  0.,  5.,  0.,  7.,  0.,  0.,  0.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0., 12.,  0.,  0.,  0.,  0.],\n        [ 1.,  0.,  1.,  0.,  1.,  0.,  7.,  0.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  1.,  0., 14.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., 16.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  2.,  0.,  7.]])\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x1440 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print('accuracy=', accuracy)\n",
    "print('confusion_matrix=\\n', cm)\n",
    "cm_numpy = cm.numpy()\n",
    "class_nums = cm_numpy.sum(axis=0)\n",
    "plt.figure(figsize=(5, 20))\n",
    "plt.imshow(cm_numpy/class_nums, interpolation='nearest', cmap=plt.cm.Blues, vmin=0, vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "增加一个隐藏层的多层感知机准确率（0.8750）相比softmax回归准确率（0.8594）有绝对1.56%的效果提升，而且是在其他参数没有任何改变的情况下。"
   ]
  }
 ]
}